Inverse Design of Plasmonic Nanohole Arrays by Combing Spectra and Structural Color in Deep Learning

Abstract

Herein, deep learning (DL) is used to predict the structural parameters of Ag nanohole arrays (NAs) for spectrum‐driving and color‐driving plasmonic applications. A dataset of transmission spectra and structural parameters of NAs is generated using finite‐difference time‐domain (FDTD) calculations and is converted to vivid structural colors using the corresponding transmission spectrum. A bidirectional neural network is used to train the transmission spectrum and structural color together. The accuracy of predicting the structural parameters using a desired spectrum is tested and found to be up to 0.99, with a determination coefficient of reproducing the desired spectrum and color to be 0.97 and 0.96, respectively. These values are higher compared to those when only training for spectrum, but requiring less training time. This strategy is able to inverse design the NAs in less than 1 s to maximize surface‐enhanced Raman scattering (SERS) enhancement by matching transmission resonance and laser excitation wavelength, and accurately regenerate colored images in 7.5 s, allowing for nanoscale printing at a resolution of approximately 100 000 dots in−1. This work has important implications for the efficient design of nanostructures for various plasmonic applications, such as plasmonic sensors, optical filters, metal‐enhanced fluorescence, SERS, and super‐resolution displays.